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Hospital Discharge Data

The Syndromic Surveillance Program (SSP) of the Acute Disease Epidemiology Section of the Georgia Division of Public Health, provides electronic influenza- like- illness (ILI) data to the Center for Disease Control and Prevention’s Influenza-like Illness Surveillance Network Program that characterizes the burden of influenza in states on a weekly basis.

ILI is defined as a fever of 1001, plus a cough or sore throat. This definition is used to classify ILI by the SSP, as well as in diagnosis at the pediatric hospital system. During the 2009 H1N1 pandemic, the SSP was provided a daily data transfer to the Center for Disease Control and Prevention to heighten situational awareness of the burden of ILI in Georgia. Throughout the peak of the pandemic, data from the pediatric hospital system identified when the percentage of daily visits for ILI had substantively increased. The data includes patient chief complaint (CC) data from emergency department visits for two facilities at Facilities A and B. The data received by SSP does not include diagnosis data.

Patient emergency department discharge data (DD) for ‘FLU’ was provided to SSP retrospectively to compare with the CC data routinely collected and analyzed. The data was derived from the pediatric health system’s month end, internal, syndromic surveillance report based upon emergency department visits, and including physician’s diagnosis at the time of patient’s discharge. The case definition of ‘FLU’ from the pediatric health system facilities is acute onset of fever, with cough and/or sore throat in the absence of a known cause other than influenza.

 

Objective

The objective of this study is to describe the difference between patient CC, ILI data provided daily to the Georgia SSP during the 2009 H1N1 pandemic, and patient DD subsequently provided for comparison with the SSP from its participating pediatric hospital system, and its two affiliated emergency rooms.

Referenced File
Submitted by hparton on

Emergency Department (ED) syndromic surveillance data for influenza-like illness (ILI) have been found to provide timely and representative information about current influenza activity in NYC. DOHMH monitors visits daily from 50 of 61 EDs, capturing about 94% of all ED visits in NYC. Since January 1, 2007, DOHMH has been receiving disposition data (e.g., hospitalized, discharged) from a subset of EDs. Currently, disposition data is received from 37 EDs (approximately 1/3 of all visits by the next day and >60% of all visits within 1 week).

More detailed hospitalization data, including date, demographics, and diagnosis on all NYC hospitalizations are routinely collected by the New York State Department of Health Statewide Planning and Research Cooperative System (SPARCS). SPARCS is subject to a 2-3 year reporting lag, thus limiting its timeliness and prospective use. However, SPARCS data from prior to January 1, 2007 can supplement the ED syndromic data to develop a model for ILI hospitalizations and calculate excess hospitalizations attributable to influenza that can be used in near realtime, particularly in the event of a pandemic.

 

Objective

To use ED syndromic surveillance data to monitor hospitalizations for ILI and calculate excess hospitalizations attributable to influenza.

Referenced File
Submitted by elamb on

Objective

Understanding the baseline dynamics of syndrome counts is essential for use in prospective syndromic surveillance. Therefore we studied to what extent the known seasonal dynamics of gastro-intestinal (GI) pathogens explain the dynamics in GI syndrome in general practitioner and hospital data.

 

Referenced File
Submitted by elamb on

Hospital syndromic surveillance data may be a useful tool in detecting increases in influenza-like-illness (ILI) and for monitoring seasonal trends or pandemic activity on a local level. A previous comparison of hospital syndromic surveillance data with ILI surveillance data manually abstracted from emergency department notes revealed that the general respiratory category performed better than symptomspecific subcategories. However, only about half of all patients hospitalized for influenza meet the ILI criteria defined as fever and either cough or sore throat. Hospital discharge data are used retrospectively to determine disease burden, but is not of use for acute monitoring due to the substantial lag time. Knowing how accurately admission data reflect discharge data can assist with interpretation of real or near-real time data streams commonly used in syndromic surveillance systems.

 

Objective

Timely unplanned hospital admissions data in a general respiratory syndrome category and/or with a pneumonia or influenza admission diagnosis are compared with hospital discharge data to determine accuracy for prediction of influenza disease burden.

Referenced File
Submitted by elamb on

Extreme heat events caused by high environmental temperatures are considered a major cause of weather-related deaths and injury in the United States. These events can result in a spectrum of conditions known as heat-related illnesses (HRIs), which range from minor to life threating symptoms. In Arizona, HRIs account for more than 2,000 emergency room visits and 118 deaths each year. In 2012, there were a total of 1,572 emergency department visits related to HRIs.

Submitted by Anonymous on

Extreme heat is a major cause of weather-related morbidity and mortality in the United States (US).1 HRI is the most frequent cause of environmental exposure-related injury treated in US emergency departments.2 More than 65,000 emergency room visits occur for acute HRI each summer nationwide.3 In Arizona, HRI accounts for an estimated 2,000 emergency room patients and 118 deaths each year.4 As heat-related illness becomes increasingly recognized as a public health issue, local health departments are tasked with building capacity to conduct enhanced surveillance of HRI in order to inform public health preparedness and response efforts. In Pinal County, understanding the magnitude and risk factors of HRI is important for informing prevention efforts as well as developing strategies to respond to extreme heat.

Objective:

Using a syndromic surveillance system to understand the magnitude and risk factors related to heat-related illness (HRI) in Pinal County, AZ.

Submitted by elamb on
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